Uinta Basin Single-Well Model To Optimize Tight Gas Completions
Bibliographic record
Abstract
Abstract In this paper, we will present an integrated single well modeling (SWM) technique to predict reservoir and completion performance for a Uinta basin development program. This technique has proven to be vital in the economic success of wells in the Uinta Basin. The integrated SWM involves the development of a petrophysical and a mechanical stress model calibrated from offset nearby wells to match well production and fracturing treatments response. The SWM is coupled with the development of NPV optimization models for each well. Tools for the validation of the SWM such as production logs, pressure measurements, and formation micro-imager (FMI) have not only been crucial in model validation, but also in order to: Evaluate production contributions based on backpressureEvaluate drainage area (from multiple production logs)Understand geological setting and production mechanismDetect scaling problems and optimize treatment solutionsUnderstand limited entryIdentify water producing zonesDevelop commingling production strategy Since a typical completion in the Uinta Basin may contain multiple producing sands (as many as 30), optimizing the completion strategy may be a challenging task. Completions are designed to optimize the production from each stimulation stage. This is achieved by calibrating the pre-stimulation injection tests on these stages using theoretical fracturing models such as pseudo-3D (P3D). Pressure history matching of fracturing data and production data provides the feedback information necessary to validate the models and improve predictability, thus improving the quality of decisions made subsequently. This process is continuous and models are updated or changed according to the geological and petrophysical setting. After the wells are completed, the use of production logs in stacked pay reservoirs enables the use of rate-&-pressure transient analysis and history matching techniques to evaluate fracture and reservoir properties. The use of these tools early in the drilling program has resulted in changes to completion and production strategies that have resulted in significant cost saving via modifications to fluid technology, proppant selection, proppant flowback control methods, commingling strategies, scale inhibition and drilling locations. Over all, the application of appropriate evaluation tools based on geological setting and appropriate information gathering has resulted in production increases of up to 30% above the previous baseline, with significant improvement in well economics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".